





'''simple docstring'''






import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline







@pytest.mark.integration
def 					_a						(       ) ->				Tuple:



			"""simple docstring"""


			with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
						with pytest.raises(_lowerCamelCase ):
									requests.request("""GET"""    ,	"""https://huggingface.co""" )
						with pytest.raises(requests.exceptions.ConnectTimeout ):
									requests.request("""GET"""    ,	"""https://huggingface.co"""    ,	timeout=1.0 )







@pytest.mark.integration
def 					_a						(       ) ->				Tuple:



			"""simple docstring"""


			with offline(OfflineSimulationMode.CONNECTION_FAILS ):
						with pytest.raises(requests.exceptions.ConnectionError ):
									requests.request("""GET"""    ,	"""https://huggingface.co""" )







def 					_a						(       ) ->				Any:



			"""simple docstring"""


			with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
						with pytest.raises(_lowerCamelCase ):
									http_head("""https://huggingface.co""" )






import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__UpperCamelCase     		=							logging.getLogger()

__UpperCamelCase     		=							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						os.makedirs(snake_case   ,    exist_ok=snake_case  )
						UpperCamelCase__       =							{"source": "What is love ?", "target": "life"}
						UpperCamelCase__       =							{"train": 12, "val": 2, "test": 2}
						for split in ["train", "test", "val"]:
									for field in ["source", "target"]:
												UpperCamelCase__       =							"\n".join([contents[field]] * n_lines[split]  )
												with open(os.path.join(snake_case   ,    F'''{split}.{field}'''  )   ,    "w"  ) as f:
															f.write(snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = "pytorch"  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_auto_remove_tmp_dir()
						UpperCamelCase__       =							os.path.join(snake_case   ,    "output"  )
						UpperCamelCase__       =							os.path.join(snake_case   ,    "data"  )
						self._create_dummy_data(data_dir=snake_case  )

						UpperCamelCase__       =							F'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

						if gpus > 0:
									testargs.append(F'''--gpus={gpus}'''  )
									if is_apex_available():
												testargs.append("--fp16"  )
						else:
									testargs.append("--gpus=0"  )
									testargs.append("--distributed_backend=ddp_cpu"  )
									testargs.append("--num_processes=2"  )

						UpperCamelCase__       =							[sys.executable, str(Path(finetune_rag.__file__  ).resolve()  )] + testargs
						execute_subprocess_async(snake_case   ,    env=self.get_env()  )

						UpperCamelCase__       =							os.path.join(snake_case   ,    "metrics.json"  )
						with open(snake_case  ) as f:
									UpperCamelCase__       =							json.load(snake_case  )
						return result




			@require_torch_gpu
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self._run_finetune(gpus=1  )
						self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,    0.2  )




			@require_torch_multi_gpu
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self._run_finetune(gpus=2  )
						self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,    0.2  )




			@require_torch_gpu
			@require_ray
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self._run_finetune(gpus=1   ,    distributed_retriever="ray"  )
						self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,    0.2  )







			@require_torch_multi_gpu
			@require_ray
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self._run_finetune(gpus=1   ,    distributed_retriever="ray"  )
						self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,    0.2  )


